diff --git a/nucliadb/src/nucliadb/ingest/orm/knowledgebox.py b/nucliadb/src/nucliadb/ingest/orm/knowledgebox.py
index 0e78bf2cc8..49079bbcde 100644
--- a/nucliadb/src/nucliadb/ingest/orm/knowledgebox.py
+++ b/nucliadb/src/nucliadb/ingest/orm/knowledgebox.py
@@ -477,13 +477,23 @@ async def create_vectorset(self, config: knowledgebox_pb2.VectorSetConfig):
         ):
             raise VectorSetConflict(f"Vectorset {config.vectorset_id} already exists")
         await datamanagers.vectorsets.set(self.txn, kbid=self.kbid, config=config)
+
+        # Remove the async deletion mark if it exists, just in case there was a previous deletion
+        deletion_mark_key = KB_VECTORSET_TO_DELETE.format(kbid=self.kbid, vectorset=config.vectorset_id)
+        deletion_mark = await self.txn.get(deletion_mark_key, for_update=True)
+        if deletion_mark is not None:
+            await self.txn.delete(deletion_mark_key)
+
         shard_manager = get_shard_manager()
         await shard_manager.create_vectorset(self.kbid, config)
 
     async def delete_vectorset(self, vectorset_id: str):
         await datamanagers.vectorsets.delete(self.txn, kbid=self.kbid, vectorset_id=vectorset_id)
+
         # mark vectorset for async deletion
-        await self.txn.set(KB_VECTORSET_TO_DELETE.format(kbid=self.kbid, vectorset=vectorset_id), b"")
+        deletion_mark_key = KB_VECTORSET_TO_DELETE.format(kbid=self.kbid, vectorset=vectorset_id)
+        await self.txn.set(deletion_mark_key, b"")
+
         shard_manager = get_shard_manager()
         await shard_manager.delete_vectorset(self.kbid, vectorset_id)
 
diff --git a/nucliadb/src/nucliadb/writer/vectorsets.py b/nucliadb/src/nucliadb/writer/vectorsets.py
index 260eebd950..6696d84181 100644
--- a/nucliadb/src/nucliadb/writer/vectorsets.py
+++ b/nucliadb/src/nucliadb/writer/vectorsets.py
@@ -47,10 +47,6 @@
 from nucliadb_utils.utilities import get_storage
 
 
-class EmbeddingNotFound(Exception):
-    pass
-
-
 async def add(kbid: str, vectorset_id: str) -> None:
     # First off, add the vectorset to the learning configuration if it's not already there
     lconfig = await learning_proxy.get_configuration(kbid)